Intelligence as a Malleable Construct

  • Lisa S. Blackwell
  • Sylvia Rodriguez
  • Belén Guerra-Carrillo


In this chapter, the authors describe research showing that people’s mindsets, or their “implicit theories,” about intelligence significantly impact their academic motivation and performance, sometimes in counter-intuitive ways. In particular, accumulating evidence shows that holding a “growth mindset”—the belief in intelligence as malleable as opposed to fixed—enhances challenge-seeking, interest in learning, effort investment, use of effective strategies, achievement outcomes, and even how the brain functions. They discuss converging evidence for the malleability of intelligence drawn from recent research in cognitive neuroscience that indicates greater brain plasticity and development resulting from learning than previously thought to exist, as shown by the impact of training on executive functions and fluid intelligence. Finally, they discuss how mindsets can be influenced and changed, and the practical implications of this research for educational policy and practice.


Mindset Motivation Achievement Brain plasticity Malleable intelligence Implicit theories Executive functions Cognitive training IQ Ability 


  1. Aronson, J., Lustina, M., Good, C., Keough, K., Steele, C., & Brown, J. (1999). When white men can’t do math: Necessary and sufficient factors in stereotype threat. Journal of Experimental Social Psychology, 35, 29–46.CrossRefGoogle Scholar
  2. Aronson, J., Fried, C. B., & Good, C. (2002). Reducing the effects of stereotype threat on African American college students by shaping mindsets of intelligence. Journal of Experimental Social Psychology, 38, 113–125.CrossRefGoogle Scholar
  3. Bartels, M., Rietveld, M., Van Baal, G., & Boomsma, D. (2002). Genetic and environmental influences on the development of intelligence. Behavior Genetics, 32, 237–249.PubMedCrossRefGoogle Scholar
  4. Beilock, S. L., Rydell, R. J., & McConnell, A. R. (2007). Stereotype threat and working memory: Mechanisms, alleviations, and spillover. Journal of Experimental Psychology: General, 136, 256–276.CrossRefGoogle Scholar
  5. Binet, A. (1975). Modern ideas about children. (trans: Heisler, S.). Menlo Park: Suzanne Heisler. (originally work published in 1909).Google Scholar
  6. Blackwell, L., Trzesniewski, K., & Dweck, C. S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78, 246–263.PubMedCrossRefGoogle Scholar
  7. Blascovich, J., Spencer, S. J., Quinn, D. M., & Steele, C. M. (2001). African-Americans and high blood pressure: The Role of stereotype threat. Psychological Science, 12, 225–229.PubMedCrossRefGoogle Scholar
  8. Borland, J. H. (2003). The death of giftedness. In J. H. Borland (Ed.), Rethinking gifted education (pp. 105–124). New York: Teachers College Press.Google Scholar
  9. Borland, J. H. (2005). Gifted education without gifted children: The case for no conception of giftedness. In R. J. Sternberg & J. E. Davidson (Eds.), Conceptions of giftedness (2nd ed.). New York: Cambridge University Press.Google Scholar
  10. Borland, J. H., & Wright, L. (2001). Identifying and educating poor and underrepresented gifted students. In K. A. Heller, F. J. Monks, R. J. Sternberg, & R. F. Subotnik (Eds.), International handbook of research and development of giftedness and talent (pp. 587–594). London: Pergamon Press.Google Scholar
  11. Brehmer, Y., Westerberg, H., & Backman, L. (2012). Working-memory training in younger and older adults: Training gains, transfer, and maintenance. Frontiers in Human Neuroscience, 6, 1–7.CrossRefGoogle Scholar
  12. Brescoll, V. L., & LaFrance, M. (2004). The correlates and consequences of newspaper reports of research on gender differences. Psychological Science, 15(8), 515–521.PubMedCrossRefGoogle Scholar
  13. Breslau, N., Chilcoat, H., Susser, E., Matte, T., Liang, K., & Peterson, E. (2001). Stability and change in children’s intelligence quotient scores: A comparison of two socioeconomically disparate communities. American Journal of Epidemiology, 154, 711–717. doi: 10.1093/aje/154.8.711.PubMedCrossRefGoogle Scholar
  14. Brown, R. P., & Day, E. A. (2006). The difference isn’t black and white: Stereotype threat and the race gap on Raven’s advanced progressive matrices. Journal of Applied Psychology, 91, 979–985.PubMedCrossRefGoogle Scholar
  15. Cadinu, M., Maass, A., Rosabianca, A., & Kiesner, J. (2005). Why do women underperform under stereotype threat? Psychological Science, 16, 572–578.PubMedCrossRefGoogle Scholar
  16. Canivez, G., & Watkins, M. (1998). Long-term stability of the Wechsler intelligence scale for children—third edition. Psychological Assessment, 10, 285–291.CrossRefGoogle Scholar
  17. Ceci, S. J. (1991). How much does schooling influence general intelligence and its cognitive components? A reassessment of the evidence. Developmental Psychology, 27(5), 703–722.CrossRefGoogle Scholar
  18. Cimpian, A., Mu, Y., & Erickson, L. C. (2012). Who is good at this game? Linking an activity to a social category undermines children’s achievement. Psychological Science, 23(5), 533–541.PubMedCrossRefGoogle Scholar
  19. Darling-Hammond, L. (1994). Performance-based assessment and educational equity. Harvard Educational Review, 64, 5–31.Google Scholar
  20. Darling-Hammond, L. (1995). Cracks in the bell curve: How education matters. The Journal of Negro Education, 64, 340–353.CrossRefGoogle Scholar
  21. Dar-Nimrod, I., & Heine, S. J. (2006). Exposure to scientific theories affects women’s math performance. Science, 314, 435.PubMedCrossRefGoogle Scholar
  22. Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., & May, A. (2004). Neuroplasticity: Changes in grey matter induced by training. Nature, 427, 311–312.PubMedCrossRefGoogle Scholar
  23. Duckworth, A., & Seligman, M. (2005). Self-discipline outdoes IQ in predicting academic performance of adolescents. Psychological Science, 16, 939–944.PubMedCrossRefGoogle Scholar
  24. Dweck, C. S. (1999). Self-theories: Their role in motivation, personality, and development. Philadelphia: Psychology Press.Google Scholar
  25. Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to motivation and personality. Psychological Review, 95, 256–273.CrossRefGoogle Scholar
  26. Erikson, K., Drevets, W., & Schulkin, J. (2003). Glucocorticoid regulation of diverse cognitive functions in normal and pathological emotional states. Neuroscience and Biobehavioral Reviews, 27, 233–246.CrossRefGoogle Scholar
  27. Farrington, C. A., Roderick, M., Allensworth, E., Nagaoka, J., Keyes, T. S., Johnson, D. W., & Beechum, N. O. (2012). Teaching adolescents to become learners. The role of noncognitive factors in shaping school performance: A critical literature review. Chicago: University of Chicago Consortium on Chicago School Research.Google Scholar
  28. Flynn, J. (2007). What is intelligence? New York: Cambridge University Press.CrossRefGoogle Scholar
  29. Giles, J. W., & Heyman, G. D. (2003). Preschoolers’ beliefs about the stability of antisocial behavior: Implications for navigating social challenges. Social Development, 12, 182–197.CrossRefGoogle Scholar
  30. Good, C., Aronson, J., & Inzlicht, M. (2003). Improving adolescents’ standardized test performance: An intervention to reduce the effects of stereotype threat. Applied Developmental Psychology, 24, 645–662.CrossRefGoogle Scholar
  31. Good, C., Rattan, A., & Dweck, C. (2012). Why do women opt out? Sense of belonging and women’s representation in mathematics. Journal of Personality and Social Psychology, 102(4), 700–717. doi: 10.1037/a0026659.PubMedCrossRefGoogle Scholar
  32. Gunderson, E., Gripshoever, S., Romero, C., Dweck, C., Goldin-Meadow, S., & Levine, S. (2013). Parent praise to 1- to 3- year-olds predicts children’s motivational frameworks 5 years later. Child Development, 84(5), 1526–1541. doi: 10.1111/cdev.12064.PubMedCentralPubMedCrossRefGoogle Scholar
  33. Hackman, D., & Farah, M. (2009). Socioeconomic status and the developing brain. Trends in Cognitive Science, 13(2), 65–73.CrossRefGoogle Scholar
  34. Hartley, B., & Sutton, R. (2013). A stereotype threat account of boys’ academic underachievement. Child Development, 84, 1716–1733. doi: 10.1111/cdev.12079.PubMedCrossRefGoogle Scholar
  35. Henderson, V. L., & Dweck, C. S. (1990). Motivation and achievement. In S. S. Feldman & G. R. Elliot (Eds.), At the threshold: The developing adolescent (pp. 308–329). Cambridge: Harvard University Press.Google Scholar
  36. Herrnstein, R. J., & Murray, C. (1994). The bell curve: Intelligence and class structure in American life. New York: Free Press.Google Scholar
  37. Hertzog, C., & Schaie, K. (1986). Stability and change in adult intelligence: 1. Analysis of longitudinal covariance structures. Psychology and Aging, 1, 159–171.PubMedCrossRefGoogle Scholar
  38. Heyman, G., Gee, C., & Giles, J. (2003). Preschool children’s reasoning about ability. Child Development, 74, 516–534.PubMedCrossRefGoogle Scholar
  39. Hong, Y., Chiu, C., Dweck, C., Lin, D., & Wan, W. (1999). Implicit theories, attributions, and coping: A meaning system approach. Journal of Personality and Social Psychology, 77, 588–599.CrossRefGoogle Scholar
  40. Hoyt, C., Burnette, J., & Inella, A. (2012). I can do that: The impact of implicit theories on leadership role model effectiveness. Personality and Social Psychology Bulletin, 38, 257–268. doi: 10.1177/ 0146167211427922.PubMedCrossRefGoogle Scholar
  41. Inzlicht, M., McKay, L., & Aronson, J. (2006). Stigma as ego depletion: How being the target of prejudice affects self-control. Psychological Science, 17, 262–269.PubMedCrossRefGoogle Scholar
  42. Kaufman, S. (2013). Ungifted: Intelligence redefined. New York: Basic Books.Google Scholar
  43. Keller, J., & Dauenheimer, D. (2003). Stereotype threat in the classroom: Dejection mediates the disrupting threat effect on women’s math performance. Personality and Social Psychology Bulletin, 29, 371–381.PubMedCrossRefGoogle Scholar
  44. Kray, L. J., & Haselhuhn, M. P. (2007). Implicit negotiation beliefs and performance: Experimental and longitudinal evidence. Journal of Personality and Social Psychology, 93, 49–64.PubMedCrossRefGoogle Scholar
  45. Krendl, A. C., Richeson, J. A., Kelley, W. M., & Heatherton, T. F. (2008). The negative consequences of threat: A functional magnetic resonance imaging investigation of the neural mechanisms underlying women’s underperformance in math. Psychological Science, 19, 168–175.PubMedCrossRefGoogle Scholar
  46. Lupien, S., McEwen, B., Gunnar, M., & Heim, C. (2009). Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nature Reviews Neuroscience, 10, 434–445.PubMedCrossRefGoogle Scholar
  47. Mackey, A. P., Hill, S., Stone, S., & Bunge, S. A. (2011). Differential effects of reasoning and speed training in children. Developmental Science, 14(3), 582–590. doi: 10.1111/j.1467-7687.2010.01005.x.PubMedCrossRefGoogle Scholar
  48. Mackey, A., Whitaker, K., & Bunge, S. (2012). Experience-dependent plasticity in white matter microstructure: reasoning training alters structural connectivity. Frontiers in Neuroanatomy, 6, 1–9. doi: 10.3389/fnana.2012.00032.CrossRefGoogle Scholar
  49. Mackey, A., Miller-Singley, A., & Bunge, S. (2013). Intensive reasoning training alters patterns of brain connectivity at rest. Journal of Neuroscience, 33(11), 4796–4803.PubMedCentralPubMedCrossRefGoogle Scholar
  50. Mangels, J. A., Butterfield, B., Lamb, J., Good, C. D., & Dweck, C. S. (2006). Why do beliefs about intelligence influence learning success? A social-cognitive-neuroscience model. Social Cognitive and Affective Neuroscience, 1, 75–86.PubMedCentralPubMedCrossRefGoogle Scholar
  51. Marx, D. M., & Stapel, D. A. (2006). Distinguishing stereotype threat from priming effects: On the role of the social self and threat-based concerns. Journal of Personality and Social Psychology, 91, 243–254.PubMedCrossRefGoogle Scholar
  52. Mischel, W., Ayduk, O., Berman, M., Casey, B., Gotlib, J., Kross, E., Teslovich, T., Wilson, N., Zayas, V., & Shoda, Y. (2011). Willpower over the lifespan: Decomposing self-regulation. Social Cognitive and Affective Neuroscience, 6(2), 252–256.PubMedCentralPubMedCrossRefGoogle Scholar
  53. Moser, J., Schroder, H., Heeter, C., Moran, T., & Lee, Y. (2011). Mind your errors: Evidence for a neural mechanism linking growth mindset to adaptive post-error adjustments. Psychological Science, 22(12), 1484–1489.PubMedCrossRefGoogle Scholar
  54. Mueller, C. M., & Dweck, C. S. (1998). Intelligence praise can undermine motivation and performance. Journal of Personality and Social Psychology, 75, 33–52.PubMedCrossRefGoogle Scholar
  55. Naglieri, J., & Goldstein, S. (2009). Practicioner’s guide to assessing intelligence and achievement. Hoboken: Wiley.Google Scholar
  56. Neville, H., Stevens, C., Pakulak, E., Bell, T., Fanning, J., Klein, S., & Isbell, E. (2013). Family-based training program improves brain function, cognition, and behavior in lower socioeconomic status preschoolers. Proceedings of the National Academy of Sciences, 110, 12138–12143. doi: 10.1073/pnas.1304437110.CrossRefGoogle Scholar
  57. Nisbett, R. (2009). Intelligence and how to get it: Why schools and cultures count. New York: W.W. Norton & Company.Google Scholar
  58. O’Brien, L. T., & Crandall, C. S. (2003). Stereotype threat and arousal: Effects on women’s math performance. Personality and Social Psychology Bulletin, 29, 782–789.PubMedCrossRefGoogle Scholar
  59. Osborne, J. W. (2006). Gender, stereotype threat and anxiety: Psychophysiological and cognitive evidence. Journal of Research in Educational Psychology, 8, 109–138.Google Scholar
  60. Osborne, J. W. (2007). Linking stereotype threat and anxiety. Educational Psychology, 27, 135–154.CrossRefGoogle Scholar
  61. Pomerantz, E. M., & Kempner, S. G. (2013). Mothers’ daily person and process praise: Implications for children’s theory of intelligence and motivation. Developmental Psychology. Google Scholar
  62. Rattan, A., Good, C., & Dweck, C. S. (2012). “It’s ok – not everyone can be good at math:” Instructors with an entity theory comfort (and demotivate) students. Journal of Experimental Social Psychology, 48, 731–737.CrossRefGoogle Scholar
  63. Rheinberg, F., Vollmeyer, R., & Rollett, W. (2000). Motivation and action in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation: Theory, research and application (pp. 503–529). San Diego: Academic.CrossRefGoogle Scholar
  64. Robins, R. W., & Pals, J. L. (2002). Implicit self-theories in the academic domain: Implications for goal orientation, attributions, affect, and self-esteem change. Self and Identity, 1, 313–336.CrossRefGoogle Scholar
  65. Rodriguez, S., Mangels, J., Guerra-Carrillo, B., & Higgins, T. (2014). Frame of mind: Focusing students on performance or mastery yields a double dissociation of the neural processes predicting subsequent memory. Manuscript in preparation.Google Scholar
  66. Schmader, T., & Johns, M. (2003). Converging evidence that stereotype threat reduces working memory capacity. Journal of Personality and Social Psychology, 85, 440–452.PubMedCrossRefGoogle Scholar
  67. Smiley, P. A., & Dweck, C. S. (1994). Individual differences in achievement goals among young children. Child Development, 65, 1723–1743.PubMedCrossRefGoogle Scholar
  68. Smith, J. L., & White, P. H. (2002). An examination of implicitly activated, explicitly activated, and nullified stereotypes on mathematical performance: It’s not just a woman’s issue. Sex Roles, 47, 179–191.CrossRefGoogle Scholar
  69. Steele, C., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African americans. Journal of Personality and Social Psychology, 69, 797–811.PubMedCrossRefGoogle Scholar
  70. Stone, J., & McWhinnie, C. (2008). Evidence that blatant versus subtle stereotype threat cues impact performance through dual processes. Journal of Experimental Social Psychology, 44, 445–452.CrossRefGoogle Scholar
  71. Thoman, D. B., White, P. H., Yamawaki, N., & Koishi, H. (2008). Variations of gender-math stereotype content affect women’s vulnerability to stereotype threat. Sex Roles, 58, 702–712.CrossRefGoogle Scholar
  72. Vick, S. B., Seery, M. D., Blascovich, J., & Weisbuch, M. (2008). The effect of gender stereotype activation on challenge and threat motivational states. Journal of Experimental Social Psychology, 44, 624–630.CrossRefGoogle Scholar
  73. Wang, M., Eccles, J., & Kenny, S. (2013). Not lack of ability but more choice: Individual and gender differences in choice of careers in science, technology, engineering, and mathematics. Psychological Science, 24(5), 770–775.PubMedCrossRefGoogle Scholar
  74. Wolf, T. (1973). Alfred Binet. Chicago: University of Chicago Press.Google Scholar
  75. Yeager, D., Paunesku, D., Walton, G., & Dweck, C. (2013). How can we instill productive mindsets at scale? A review of the evidence and an initial R&D agenda. [white paper]. Retrieved from: http://homepage. 20-%206-10-13.pdf
  76. Zatorre, R., Fields, R., & Johansen-Berg, H. (2012). Plasticity in gray and white: Neuroimaging changes in brain structure during learning. Nature Neuroscience, 15(4), 528–536. doi: 10.1038/nn.3045.PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Lisa S. Blackwell
    • 1
  • Sylvia Rodriguez
    • 1
  • Belén Guerra-Carrillo
    • 2
  1. 1.Mindset Works, Inc.WalnutUSA
  2. 2.University of California at BerkeleyBerkeleyUSA

Personalised recommendations